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| import cv2 | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from basicsr.metrics.metric_util import reorder_image, to_y_channel | |
| from basicsr.utils.color_util import rgb2ycbcr_pt | |
| from basicsr.utils.registry import METRIC_REGISTRY | |
| def calculate_psnr(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): | |
| """Calculate PSNR (Peak Signal-to-Noise Ratio). | |
| Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio | |
| Args: | |
| img (ndarray): Images with range [0, 255]. | |
| img2 (ndarray): Images with range [0, 255]. | |
| crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. | |
| input_order (str): Whether the input order is 'HWC' or 'CHW'. Default: 'HWC'. | |
| test_y_channel (bool): Test on Y channel of YCbCr. Default: False. | |
| Returns: | |
| float: PSNR result. | |
| """ | |
| assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') | |
| if input_order not in ['HWC', 'CHW']: | |
| raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') | |
| img = reorder_image(img, input_order=input_order) | |
| img2 = reorder_image(img2, input_order=input_order) | |
| if crop_border != 0: | |
| img = img[crop_border:-crop_border, crop_border:-crop_border, ...] | |
| img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] | |
| if test_y_channel: | |
| img = to_y_channel(img) | |
| img2 = to_y_channel(img2) | |
| img = img.astype(np.float64) | |
| img2 = img2.astype(np.float64) | |
| mse = np.mean((img - img2)**2) | |
| if mse == 0: | |
| return float('inf') | |
| return 10. * np.log10(255. * 255. / mse) | |
| def calculate_psnr_pt(img, img2, crop_border, test_y_channel=False, **kwargs): | |
| """Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). | |
| Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio | |
| Args: | |
| img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). | |
| img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). | |
| crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. | |
| test_y_channel (bool): Test on Y channel of YCbCr. Default: False. | |
| Returns: | |
| float: PSNR result. | |
| """ | |
| assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') | |
| if crop_border != 0: | |
| img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] | |
| img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] | |
| if test_y_channel: | |
| img = rgb2ycbcr_pt(img, y_only=True) | |
| img2 = rgb2ycbcr_pt(img2, y_only=True) | |
| img = img.to(torch.float64) | |
| img2 = img2.to(torch.float64) | |
| mse = torch.mean((img - img2)**2, dim=[1, 2, 3]) | |
| return 10. * torch.log10(1. / (mse + 1e-8)) | |
| def calculate_ssim(img, img2, crop_border, input_order='HWC', test_y_channel=False, **kwargs): | |
| """Calculate SSIM (structural similarity). | |
| ``Paper: Image quality assessment: From error visibility to structural similarity`` | |
| The results are the same as that of the official released MATLAB code in | |
| https://ece.uwaterloo.ca/~z70wang/research/ssim/. | |
| For three-channel images, SSIM is calculated for each channel and then | |
| averaged. | |
| Args: | |
| img (ndarray): Images with range [0, 255]. | |
| img2 (ndarray): Images with range [0, 255]. | |
| crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. | |
| input_order (str): Whether the input order is 'HWC' or 'CHW'. | |
| Default: 'HWC'. | |
| test_y_channel (bool): Test on Y channel of YCbCr. Default: False. | |
| Returns: | |
| float: SSIM result. | |
| """ | |
| assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') | |
| if input_order not in ['HWC', 'CHW']: | |
| raise ValueError(f'Wrong input_order {input_order}. Supported input_orders are "HWC" and "CHW"') | |
| img = reorder_image(img, input_order=input_order) | |
| img2 = reorder_image(img2, input_order=input_order) | |
| if crop_border != 0: | |
| img = img[crop_border:-crop_border, crop_border:-crop_border, ...] | |
| img2 = img2[crop_border:-crop_border, crop_border:-crop_border, ...] | |
| if test_y_channel: | |
| img = to_y_channel(img) | |
| img2 = to_y_channel(img2) | |
| img = img.astype(np.float64) | |
| img2 = img2.astype(np.float64) | |
| ssims = [] | |
| for i in range(img.shape[2]): | |
| ssims.append(_ssim(img[..., i], img2[..., i])) | |
| return np.array(ssims).mean() | |
| def calculate_ssim_pt(img, img2, crop_border, test_y_channel=False, **kwargs): | |
| """Calculate SSIM (structural similarity) (PyTorch version). | |
| ``Paper: Image quality assessment: From error visibility to structural similarity`` | |
| The results are the same as that of the official released MATLAB code in | |
| https://ece.uwaterloo.ca/~z70wang/research/ssim/. | |
| For three-channel images, SSIM is calculated for each channel and then | |
| averaged. | |
| Args: | |
| img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). | |
| img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). | |
| crop_border (int): Cropped pixels in each edge of an image. These pixels are not involved in the calculation. | |
| test_y_channel (bool): Test on Y channel of YCbCr. Default: False. | |
| Returns: | |
| float: SSIM result. | |
| """ | |
| assert img.shape == img2.shape, (f'Image shapes are different: {img.shape}, {img2.shape}.') | |
| if crop_border != 0: | |
| img = img[:, :, crop_border:-crop_border, crop_border:-crop_border] | |
| img2 = img2[:, :, crop_border:-crop_border, crop_border:-crop_border] | |
| if test_y_channel: | |
| img = rgb2ycbcr_pt(img, y_only=True) | |
| img2 = rgb2ycbcr_pt(img2, y_only=True) | |
| img = img.to(torch.float64) | |
| img2 = img2.to(torch.float64) | |
| ssim = _ssim_pth(img * 255., img2 * 255.) | |
| return ssim | |
| def _ssim(img, img2): | |
| """Calculate SSIM (structural similarity) for one channel images. | |
| It is called by func:`calculate_ssim`. | |
| Args: | |
| img (ndarray): Images with range [0, 255] with order 'HWC'. | |
| img2 (ndarray): Images with range [0, 255] with order 'HWC'. | |
| Returns: | |
| float: SSIM result. | |
| """ | |
| c1 = (0.01 * 255)**2 | |
| c2 = (0.03 * 255)**2 | |
| kernel = cv2.getGaussianKernel(11, 1.5) | |
| window = np.outer(kernel, kernel.transpose()) | |
| mu1 = cv2.filter2D(img, -1, window)[5:-5, 5:-5] # valid mode for window size 11 | |
| mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5] | |
| mu1_sq = mu1**2 | |
| mu2_sq = mu2**2 | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = cv2.filter2D(img**2, -1, window)[5:-5, 5:-5] - mu1_sq | |
| sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq | |
| sigma12 = cv2.filter2D(img * img2, -1, window)[5:-5, 5:-5] - mu1_mu2 | |
| ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2)) | |
| return ssim_map.mean() | |
| def _ssim_pth(img, img2): | |
| """Calculate SSIM (structural similarity) (PyTorch version). | |
| It is called by func:`calculate_ssim_pt`. | |
| Args: | |
| img (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). | |
| img2 (Tensor): Images with range [0, 1], shape (n, 3/1, h, w). | |
| Returns: | |
| float: SSIM result. | |
| """ | |
| c1 = (0.01 * 255)**2 | |
| c2 = (0.03 * 255)**2 | |
| kernel = cv2.getGaussianKernel(11, 1.5) | |
| window = np.outer(kernel, kernel.transpose()) | |
| window = torch.from_numpy(window).view(1, 1, 11, 11).expand(img.size(1), 1, 11, 11).to(img.dtype).to(img.device) | |
| mu1 = F.conv2d(img, window, stride=1, padding=0, groups=img.shape[1]) # valid mode | |
| mu2 = F.conv2d(img2, window, stride=1, padding=0, groups=img2.shape[1]) # valid mode | |
| mu1_sq = mu1.pow(2) | |
| mu2_sq = mu2.pow(2) | |
| mu1_mu2 = mu1 * mu2 | |
| sigma1_sq = F.conv2d(img * img, window, stride=1, padding=0, groups=img.shape[1]) - mu1_sq | |
| sigma2_sq = F.conv2d(img2 * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu2_sq | |
| sigma12 = F.conv2d(img * img2, window, stride=1, padding=0, groups=img.shape[1]) - mu1_mu2 | |
| cs_map = (2 * sigma12 + c2) / (sigma1_sq + sigma2_sq + c2) | |
| ssim_map = ((2 * mu1_mu2 + c1) / (mu1_sq + mu2_sq + c1)) * cs_map | |
| return ssim_map.mean([1, 2, 3]) | |